Software Solutions Provider
A global open source operating software provider, managing over 1,000 sales reps in Asia Pacific, has been using legacy sales performance management applications for processing incentives. Lack of flexibility in the application led to an increase in offline data management using spreadsheets. The inability of software to allow multiple users to work concurrently indicates that simultaneous running tasks are not possible. This resulted in sequential operations doubling the turnaround time, affecting the incentive processing and payouts. Furthermore, managing operations in this manner resulted in lack of system available-time for changing or improving existing incentive policies or incentive reports for the field.
Some of the key challenges the company faced were:
- Computed incentives in the existing solution lacked a maker-checker functionality, requiring manual validations which significantly increased operational overheads.
- Furthermore, this still did not guarantee accuracy in reps’ payouts due to prevalent human errors. Inaccurate payouts and lack of clarity in calculation resulted in an increased number of queries coming in from the field after the release of incentive statements.
- Sales reps frequently struggled to read their own incentive statements as they were not updated to be inline with plan changes.
- Operationally, it was tough to troubleshoot as the architecture did not support data isolation. This led to difficulties in tracing back differences in the payouts.
- System lacked the ability to have discrete processing which meant even a minor change needed a full run of the system, almost taking 8-9 hours. It slowed down the testing process and increased redundant tasks to be done offline.
- Lastly, only one user could run the system at a time, which meant the rest of the users handling different parts of the sales performance management function had to take turns, leading to an increase in operations time.
- Issues across all the functional areas within sales performance management division were identified
- Stakeholders for all these areas were asked to prioritize their issues in terms of criticality and need to resolve on a scale of 1 to 5
- Issues on either side of score 3 were separated out and clubbed
- As next step, interdependencies within the function and across the function were identified to accordingly plan an agile delivery cycle
- The low hanging fruits were attacked first, and while doing so operational steps associated with them were automated to save as much time as possible.
- Upstream, all the data inputs were collated and a unified structure was created, which helped organize the data ingestion process.
- Downstream, rep reports were updated to be more visual, which helped increase visibility and transparency. This further reduced the number of queries, which increased the time in hand to focus on bigger issues.
- Architecture was revised to separate out historical data. This helped reduce the processing time of the current period and also made data retrieval quicker.
- Batches were created and scheduled to run overnight, when the system was accessed the least.
Some key performance outcomes were:
- Operations today are executed in less than 50% of what it took before the above changes in data accuracy.
- Optimized architecture helped create and maintain a streamlined flow of information. This resulted in reduction as well as troubleshooting of error root causes.
- Revised reports gave better insights to reps reducing the number of queries raised asking for an explanation of the details of each component.
- Waiting time for analysts has reduced by over two-thirds and they can now evaluate the effect of the changes they made and analyze if the incentive payouts are as expected.
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